臺灣博碩士論文加值系統

在本篇論文中針對一些具有未知截止區的非線性不確定系統，設計出一輔以截止區補償的強健適應模糊控制器。截止區的特徵在致動器中是相當容易發生的，例如直流伺服馬達系統、壓電轉換器、醫用機器人及工具機械。但是，在這些例子的截止區通常欠缺完整的了解，而且可能會嚴格地限制控制的效能。因此，控制器除了要去處理系統中不確定性的強健性，同時也需要去適應那未知截止區的不確定性。 本論文藉由使用截止區的補償，以及從直覺上和數學上去探討這截止區模型的特性，一個強健適應模糊控制法則在不需要去建構截止區的反函數下被提出。基於李亞普諾夫穩定定理，所提出的控制器不僅可以確保系統在具有未知截止區的情況下之強健穩定度，而且也可以獲得良好的追蹤性能。最後，本論文將舉出兩個例題及電腦模擬的結果，來證明所提出的強健適應模糊控制器之有效性及效能。

In this thesis, a robust adaptive fuzzy control scheme with using deadzone compensation is designed for a class of nonlinear uncertain systems preceded by an unknown deadzone. Deadzone characteristics are quite commonly encountered in actuators, such as DC servo system, piezoelectric transducer, medical robot, and machine tools. However, deadzones in the above-mentioned systems are usually poorly known and may severely limit the performance of control. Therefore, controllers are required to deal with the robust stability of the system in the presence of uncertainties, and also need to adapt the unknown deadzone uncertainties. By using deadzone compensation and exploring the properties of this deadzone model intuitively and mathematically, this thesis presents a robust adaptive fuzzy control scheme without constructing the deadzone inverse. Based on Lyapunov stability theorem, the proposed controller can not only guarantee the robust stability of the whole closed-loop system with an unknown deadzone, but also obtain good tracking performance. Finally, two examples and simulation results are provided to demonstrate the effectiveness and performance of the proposed method.

TABLES OF CONTENTS

ACKNOWLEDGEMENTS I
ABSTRACT (IN ENGLISH) II
ABSTRACT (IN CHINESE) III
TABLE OF CONTENTS IV
LIST OF FIGURES V
CHAPTER
1 INTRODUCTION 1
2 PROBLEM STATEMENT AND PRELIMINARIES 4
2.1 Problem Statement 4
2.2 Description of Fuzzy Logic Systems 10
3 CONTROLLER DESIGN AND STABILITY ANALYSIS 12
3.1 Robust Adaptive Fuzzy Control 13
4 RESULTS OF SIMULATION 25
4.1 A Second-Order Nonlinear System 25
4.1.1 Robust Adaptive Fuzzy Control Approach 26
4.2 A DC servo System 33
4.2.1 Robust Adaptive Fuzzy Control Approach 34
5 CONCLUSION 41
REFERENCES 42

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